For high-dimensional data sets, we propose a method to improve sparse logic regression model by using the graph structure information between predictive variables. In this method, logic regression modeling is carried out by using high-dimensional graph structure data or overlapping group structure, it is still applicable even if the graph structure of predictive variables is unknown. When graph structure is some special forms, all current popular methods such as Adaptive Lasso, (Overlapping) Group Lasso and ridge regression can be regarded as special cases of this method. Numerical simulation and real data analysis show that the proposed method can effectively use the graph structure information of predictive variables to improve the performance of the model in estimation, prediction, variable selection and so on. Moreover, the model is effective in the case of limited samples and overcomes the problem of the dimensionality of data sets, improves the performance of the sparse logic regression model by using the graph structure of high-dimensional data, and can be widely used in disease classification of high-throughput gene data sets.